eye-disease-detection-model

Eye Disease Detection and Classification using Deep Learning

This dataset contains a trained deep learning model designed to classify and detect common eye diseases from medical images. Using a convolutional neural network (CNN) built with Fastai and PyTorch, the model provides automated, high-accuracy predictions based on retinal imagery.

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Overview

This project applies deep learning to the field of ophthalmology by leveraging a pre-trained ResNet18 architecture fine-tuned on a labeled dataset of eye images. The model is capable of distinguishing between healthy eyes and various pathological conditions, aiding in early detection and potential diagnosis.

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Problem Statement

Manual diagnosis of eye diseases can be time-consuming and resource-intensive. This model provides a step toward automation by identifying eye diseases from image data with high accuracy, potentially assisting doctors or being integrated into pre-screening tools.

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Model Details

• Architecture: ResNet-18 (pretrained on ImageNet)
• Framework: Fastai + PyTorch
• Training Method: Fine-tuned for 10 epochs using vision_learner
• Evaluation Metric: ~92% validation accuracy
• Model Output: eye_disease_model.pkl

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Eye Disease Categories

The model can classify eye images into the following categories:

• Cataract
• Glaucoma
• Retina Disease
• Normal

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Author

Ali Akbar Khan
GitHub: @aliiakbarkhan

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References

• Fastai Documentation
• PyTorch
• Eye Disease Datasets on Kaggle